Design an Online Aquarium with Intelligent Recommend System

Lee, Zne-Jung; He, Huang-Mei; Su, Zhi-Yong; Chen, Yi; Xiao, Jiaying; Yang, Jianxiu

doi:10.1109/icbaie49996.2020.00052

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Since Google released Google Knowledge Graph in 2012, knowledge graph has developed rapidly and has been widely used in various fields, such as data analysis, intelligent retrieval, intelligent recommendation, human-computer interaction, etc. (Indra & Thangaraj, 2019;Jadhav & Patil T, 2017;Lee, He, Su, Chen, Xiao, & Yang, 2020;Wu, Shen, Deng, & Cheng, 2019) As a large-scale semantic network, the knowledge graph contains entities, concepts, and various rich semantic relationships (Heist, Hertling, Ringler, & Paulheim, 2020). Compared with the traditional semantic network, the knowledge graph has the characteristics of large scale, rich semantics, good quality and structure.…”

Section: Related Workmentioning

confidence: 99%

Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs

Wang

et al. 2022

International Journal on Semantic Web and Information Systems

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In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.

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Section: Related Workmentioning

confidence: 99%